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Bayesian Asymptotic Theory in a Time Series Model with a Possible Nonstationary Process

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Author Info
Kim, Jae-Young
Abstract

Asymptotic normality of the Bayesian posterior is a well-known result for stationary dynamic models or nondynamic models. This paper extends the analysis to a time series model with a possible nonstationary process. We spell out conditions under which asymptotic normality of the posterior is obtained even if the true data-generation process is a nonstationary process.

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Publisher Info
Article provided by Cambridge University Press in its journal Econometric Theory.

Volume (Year): 10 (1994)
Issue (Month): 3-4 (August)
Pages: 764-773
Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Handle: RePEc:cup:etheor:v:10:y:1994:i:3-4:p:764-773_00

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  1. John C. Chao & Peter C.B. Phillips, 1997. "Model Selection in Partially Nonstationary Vector Autoregressive Processes with Reduced Rank Structure," Cowles Foundation Discussion Papers 1155, Cowles Foundation, Yale University. [Downloadable!]
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  2. Peter C.B. Phillips, 2003. "Laws and Limits of Econometrics," Cowles Foundation Discussion Papers 1397, Cowles Foundation, Yale University. [Downloadable!]
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  3. Penelope Smith, 2006. "Bayesian Inference for a Threshold Autoregression with a Unit Root," Melbourne Institute Working Paper Series wp2006n20, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne. [Downloadable!]
  4. Peter C.B. Phillips & Zhijie Xiao, 1998. "A Primer on Unit Root Testing," Cowles Foundation Discussion Papers 1189, Cowles Foundation, Yale University. [Downloadable!]
    Other versions:
  5. Christian Julliard, 2003. "The international diversification puzzle is not worse than you think," International Finance 0301004, EconWPA. [Downloadable!]
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